AFNI Message Board

Dear AFNI users-

We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:

https://discuss.afni.nimh.nih.gov

Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.

The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.

Sincerely, AFNI HQ

History of AFNI updates  

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Daniel Glen
May 22, 2009 03:32PM
adwarp applies an affine or 12-piecewise linear affine transformation to the original data depending on how the Talairach transformation was created. If the anatomical parent dataset was created with a manual transformation, then the transformation is defined over 12 transformations. If the anatomical parent was created with @auto_tlrc, then a single affine transformation is used.

To apply the same single affine transformation from an anatomical parent dataset to other datasets, a variety of programs can be used including adwarp, 3dWarp, 3dAllineate, @auto_tlrc and align_epi_anat.py. Each program has options for how data will be interpolated to a new grid. Interpolation is a way of resampling from the original grid to a new grid. Rick Reynolds wrote up a short description about this resampling recently on this message:

[afni.nimh.nih.gov]

For the output grid and resolution, in most cases, for EPI data, we recommend using the minimum dimension in x,y,z to avoid losing data, but you may want to use something larger. If you will be applying a large smoothing blur, then a fine resolution would not be necessary. Depending on the program, there may be options for defining the output to match the grid of a specified dataset or a cubic voxel size dimension. The programs ultimately call the same function, I believe, but the extents of the grid (the box size) and the voxel size defaults differ. Here are some examples of applying the transformation from an "auto-tlrc"-ed dataset to an EPI dataset.

adwarp -apar anat+tlrc -dpar epi_r1+orig. -prefix tt_epi_tlrc_adwarp -dxyz 3.5

cat_matvec anat+tlrc::WARP_DATA > anatwarp.1D
3dWarp -matvec_in2out anatwarp.1D -prefix tt_epi_3dWarp+tlrc epi_r1+orig.
3drefit -view tlrc tt_epi_3dWarp+orig.

3dWarp -matparent anat+tlrc -prefix tt_3dWarpmatpar_epi -gridset epi_r1+orig -overwrite epi_r1+orig.
3drefit -view tlrc tt_3dWarpmatpar_epi+orig.

# align epi data to anatomical data and create 'tlrc'ed epi too
align_epi_anat.py -anat anat+orig -tlrc_apar anat+tlrc -epi epi_r1 -epi_base 4 -epi2anat -suffix _al2anat -master_tlrc 3.5
Subject Author Posted

Adwarp process

nikki sullivan May 22, 2009 01:32PM

Re: Adwarp process

Daniel Glen May 22, 2009 03:32PM